Short-Term Load Forecasting Based on CNN and LSTM Deep Neural Networks
First Ali Agga, Second Ahmed Abbou, Yassine El Houm, Moussa Labbadi
Abstract
In the coming years, the world will witness a global transition towards the adoption of photovoltaic technology for large-scale plants to produce electricity at a grid scale, and more householders will also be encouraged to produce their electricity. However, the reliance of the photovoltaic plants on erratic weather conditions requires the development of solutions that could help in preventing any electricity blackout or overproduction. Hence, comes the role of forecasting models that help in overcoming that issue. In this work, two deep learning models are developed and tested (LSTM, CNN). Both architectures will go under several different configurations to witness the impact of changing the number of hidden layers on the accuracy of the forecasts. The findings reveal that the models behave differently when the number of layers changed over the different configurations. In addition, two-time windows were considered (1-Day, 2-Days) for even deeper insight..